It was the purpose of this thesis to investigate the utility of the bispectrum in the analysis of the human electroencephalogram (EEG). First 95% confidence thresholds were determined for the normalized bicoherence and phase bicoherence. From these 95% confidence thresholds we studied the ability of these measures to extract significant interactions in varying degrees of noise as compared to existing measures. Investigations into interactions in the visual system were carried out to demonstrate the utility of the bicoherence as a signal tracer through the nervous system. Bispectra, bicoherence, and biphase were calculated for eight subjects observing the visual stimulus monocularly. Both phase vs frequency and biphase vs frequency plots were made to determine weighted time delays from stimulus application to signal appearance at the EEG electrodes. Bispectral analysis revealed non-linear interactions between visual fields occurring at the level of the cortex with weighted delay times of 410 ± 58 msec while the non-interactive components propagated with weighted time delays of 202 ± 39 msec. Combining these results with the predictions of various existing models we conclude that the interaction does not occur in the retina. This conclusion was confirmed by a second set of experiments, in which each stimulus was presented to a different visual field in separate eyes. Investigations into the inter-ictal EEG of patients afflicted with epilepsy were carried out to determine if the side of the cortex that contained the seizure focus could be differentiated from the side that did not contain the seizure focus. The emphasis was on patients who had seizures emanating from the anterior temporal lobe. It was found that bispectral analysis of this area could not differentiate the side containing the ictal focus from the side not containing the ictal focus during the inter-ictal EEG; however during the ictal period, the bispectrum identifies harmonic order in the EEG signal that is not found either visually or in the power spectrum. These results show the bispectrum as a effective tool in the analysis of the EEG
